5-airi_convnet.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Airi-ConvNet\n",
    "Autor: Gabriel Dornelles Monteiro, abril de 2022. Notebook nº4."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vejamos o desempenho da nossa rede convolucional comparada a nossos modelos anteriores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from torchvision.datasets import CIFAR10\n",
    "\n",
    "classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files already downloaded and verified\n",
      "Files already downloaded and verified\n"
     ]
    }
   ],
   "source": [
    "# Boilerplate do notebook anterior\n",
    "train_dataset = CIFAR10(root=\"./\", download=True, train=True) \n",
    "val_dataset = CIFAR10(root=\"./\", download=True, train=False) \n",
    "\n",
    "train_images = np.array([np.array(train_dataset[i][0]) for i in range(len(train_dataset))])\n",
    "train_targets = np.array([np.array(train_dataset[i][1]) for i in range(len(train_dataset))])\n",
    "\n",
    "val_images = np.array([np.array(val_dataset[i][0]) for i in range(len(val_dataset))])\n",
    "val_targets = np.array([np.array(val_dataset[i][1]) for i in range(len(val_dataset))])\n",
    "\n",
    "original_val = val_images\n",
    "#transforma nossas imagens 32x32x3 em vetor linha 3072\n",
    "# train_images = np.reshape(train_images, (train_images.shape[0], -1))\n",
    "# val_images = np.reshape(val_images, (val_images.shape[0], -1))\n",
    "\n",
    "# média do array train_images no eixo 0, eixo onde os índices são as imagens 3072\n",
    "mean_image = np.mean(train_images, axis = 0)\n",
    "\n",
    "# mean_image é um array de floats, para operação fazer sentido nossas imagens precisam ser também.\n",
    "train_images = train_images.astype(float)\n",
    "train_images -= mean_image\n",
    "\n",
    "val_images = val_images.astype(float)\n",
    "val_images -= mean_image\n",
    "# train_images shape: (50000, 3072)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Carregamos o modelo treinado:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "import dill as pickle\n",
    "sys.path.insert(0, \"airi\")\n",
    "\n",
    "with open('airi/DemoConvNet.airi', 'rb') as f:\n",
    "    model = pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'bird': 0.41, 'cat': 0.428, 'dog': 0.461, 'deer': 0.474, 'car': 0.63, 'frog': 0.642, 'truck': 0.671, 'horse': 0.68, 'plane': 0.727, 'ship': 0.746}\n"
     ]
    }
   ],
   "source": [
    "# cria um dicionário com o nome das classes e uma lista contendo as imagens associadas no dataset de validação \n",
    "empty_lists = [ [] for _ in range(10)]\n",
    "per_class_images = dict(zip(classes,empty_lists))\n",
    "\n",
    "for idx, image in enumerate(val_images):\n",
    "    image_target = classes[val_targets[idx]]\n",
    "    per_class_images[image_target].append(image)\n",
    "\n",
    "correct = 0\n",
    "scores_dict = {}\n",
    "\n",
    "for idx, class_name in enumerate(classes):\n",
    "    correct = 0\n",
    "    for X in per_class_images[class_name]:\n",
    "        X = X[None,:,:,:]\n",
    "        softmax = model(X)\n",
    "\n",
    "        if np.argmax(softmax) == idx:\n",
    "            correct+=1\n",
    "\n",
    "    scores_dict.update({class_name: correct/1000})\n",
    "\n",
    "scores_dict = dict(sorted(scores_dict.items(), key = lambda item: item[1]))\n",
    "print(scores_dict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Temos uma média de **58.7%** de precisão geral para nossa ConvNet construida do zero. É possivel alcançar cerca de 60-62% com este modelo, se dispor do tempo necessário para treina-lo.\n",
    "\n",
    "Comparando ao modelo anterior Two Layer Neural Net:\n",
    "\n",
    "| Classe  |Precisão ConvNet|  Precisão Two Layer NN|  Precisão Softmax-Classsifier |\n",
    "|---------|---|---|---|\n",
    "| cat |0.428| 0.262  |0.211   | 5.1%\n",
    "| bird |0.41| 0.359  | 0.201  | **15.8%**\n",
    "| dog |0.461| 0.395  | 0.348  |  4.7%\n",
    "| deer |0.474|0.462   | 0.279  | **18.3%**\n",
    "| plane |  0.727|0.495  | 0.492  | 0.3%\n",
    "| truck |0.671 |0.534  | 0.499  | 3.5%\n",
    "| frog | 0.642|0.556  | 0.545  | 1.1%\n",
    "| horse | 0.68|0.568  | 0.388   | **18%**\n",
    "| car | 0.63|0.672  | 0.468  | **20.4%**\n",
    "| ship |0.746 |0.695   | 0.556  | **13.9%**\n",
    "\n",
    "Tivemos um enorme ganho de precisão na classe Plane, já que agora conseguimos definir algumas características do avião, e não só depender da cor azul, que está muito presente também nos aviões, assim como caminhões.\n",
    "\n",
    "Tivemos um ganho enorme para os gatos também, apesar de ainda não conseguirmos definir muito bem o que é um, saltamos de 26% para quase 43% de precisão.\n",
    "\n",
    "Tivemos pequenos ganhos nas outras classes e uma perda na classe carro, que era extremamente dominante nos neurônios que visualizamos da Two Layer NN\n",
    "\n",
    "---\n",
    "\n",
    "# Visualizando o que acontece dentro de uma ConvNet"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vejamos uma inferência para uma imagem qualquer de validação:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted: truck\n",
      "Probability: 0.9999966846894813\n"
     ]
    }
   ],
   "source": [
    "sample_n = 609 # 603-good bird  604-good car 609-good truck\n",
    "sample = val_images[sample_n]\n",
    "plt.imshow(original_val[sample_n])\n",
    "plt.title(classes[val_targets[sample_n]])\n",
    "plt.show()\n",
    "\n",
    "sample = sample[None,:,:,:] # add batch dimension\n",
    "softmax = model(sample)\n",
    "result = np.argmax(softmax)\n",
    "print(f\"Predicted: {classes[result]}\")\n",
    "print(f\"Probability: {softmax[0][result]}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Agora vejamos os filtros aprendidos pelo nosso modelo:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from math import sqrt, ceil\n",
    "def visualize_grid(Xs, ubound=255.0, padding=1):\n",
    "    \"\"\"\n",
    "    Reshape a 4D tensor of image data to a grid for easy visualization.\n",
    "    Inputs:\n",
    "    - Xs: Data of shape (N, H, W, C)\n",
    "    - ubound: Output grid will have values scaled to the range [0, ubound]\n",
    "    - padding: The number of blank pixels between elements of the grid\n",
    "    \"\"\"\n",
    "    (N, H, W, C) = Xs.shape\n",
    "    grid_size = int(ceil(sqrt(N)))\n",
    "    grid_height = H * grid_size + padding * (grid_size - 1)\n",
    "    grid_width = W * grid_size + padding * (grid_size - 1)\n",
    "    grid = np.zeros((grid_height, grid_width, C))\n",
    "    next_idx = 0\n",
    "    y0, y1 = 0, H\n",
    "    for y in range(grid_size):\n",
    "        x0, x1 = 0, W\n",
    "        for x in range(grid_size):\n",
    "            if next_idx < N:\n",
    "                img = Xs[next_idx]\n",
    "                low, high = np.min(img), np.max(img)\n",
    "                grid[y0:y1, x0:x1] = ubound * (img - low) / (high - low)\n",
    "                # grid[y0:y1, x0:x1] = Xs[next_idx]\n",
    "                next_idx += 1\n",
    "            x0 += W + padding\n",
    "            x1 += W + padding\n",
    "        y0 += H + padding\n",
    "        y1 += H + padding\n",
    "    return grid\n",
    "filters = model.model[0].conv\n",
    "#figure(figsize=(2, 2), dpi=30)\n",
    "grid = visualize_grid(filters.transpose(0, 2, 3, 1))\n",
    "plt.imshow(grid.astype('uint8'))\n",
    "plt.axis('off')\n",
    "#plt.gcf().set_size_inches(5, 5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Como nossos filtros são pequenos (5x5), é difícil ver o que ele está procurando, mas vejamos o que cada um deles ativa:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1000 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.pyplot import figure\n",
    "%matplotlib inline\n",
    "\n",
    "x = sample\n",
    "x = x.transpose(0,3,1,2)\n",
    "filters = model.model[0](x)[0]\n",
    "\n",
    "figure(figsize=(15, 10), dpi=100)\n",
    "for i in range(filters.shape[0]):\n",
    "    plt.subplot(4,4, i +1)\n",
    "    map_ = filters[i]\n",
    "    w_min, w_max = np.min(map_), np.max(map_)\n",
    "    wimg = 255.0 * (map_ - w_min) / (w_max - w_min)\n",
    "    plt.imshow(wimg.astype('uint8'), cmap='gray')\n",
    "    plt.title(i)\n",
    "    plt.axis(\"off\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Temos que branco são ativações altas, enquanto preto são ativações baixas.\n",
    "\n",
    "Vemos no filtro 0 por exemplo, pouca ativação na lataria do carro, já que o vermelho, não é uma cor presente na matriz do filtro 0\n",
    "\n",
    "Vejamos agora a segunda camada de convolução:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1000 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.pyplot import figure\n",
    "%matplotlib inline\n",
    "\n",
    "x = sample\n",
    "x = x.transpose(0,3,1,2)\n",
    "for layer in model.model[:3]:\n",
    "    x = layer.forward(x)\n",
    "\n",
    "filters = x[0]\n",
    "# filters = model.forward()\n",
    "\n",
    "figure(figsize=(15, 10), dpi=100)\n",
    "for i in range(filters.shape[0]):\n",
    "    plt.subplot(4,4, i +1)\n",
    "    map_ = filters[i]\n",
    "    w_min, w_max = np.min(map_), np.max(map_)\n",
    "    wimg = 255.0 * (map_ - w_min) / (w_max - w_min)\n",
    "    plt.imshow(wimg.astype('uint8'), cmap='gray')\n",
    "    plt.title(i)\n",
    "    plt.axis(\"off\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Esta é a ultima camada antes de entrarmos nas camadas lineares, e ela serve como alimentação das lineares que virão. Veja que em alguns destes neurônios, ainda vemos claramente um caminhão, e por isso o modelo nos entrega com 99% de confiança a classe correta.\n",
    "\n",
    "As camadas lineares tem como função agora meramente receber as ativações no formato como estas chegam, e mapear até a probabilidade correta, estas que já definiram caracteristicas da classe, como podemos ver a região das rodas com uma alta ativação (em branco).\n",
    "\n",
    "As convoluções tem como objetivo evidenciar características da imagem que está sendo passada ao modelo, e alimentar as camadas lineares com as caracteristicas do objeto na imagem, e não mais, de certa maneira, checando qual template gera maior ativação, como tinhamos nos modelos puramente baseado em camadadas lineares. Deste modo, as convoluções aprendem \"formas\" e \"padrões\" que ao convoluir com a imagem, evidenciem esta caracteristica e ignore o que não é relevante.\n",
    "\n",
    "Para visualizarmos melhor veremos como é por dentro de uma ConvNet treinada no ImageNet, está não treinada com imagens 32x32, mas 224x224.\n",
    "\n",
    "Artigo original: https://arxiv.org/pdf/1311.2901.pdf\n",
    "\n",
    "\n",
    "![image](https://user-images.githubusercontent.com/56324869/162348894-11c49bdf-8782-4abd-a060-972717bb33bf.png)\n",
    "\n",
    "Aqui vemos algumas formas e padrões aprendidos, como cores, gradientes, retas e outros formatos, que ao convoluir a imagem, devem evidenciar suas características\n",
    "\n",
    "![image](https://user-images.githubusercontent.com/56324869/162349059-d620f784-ee88-47a6-a2eb-33d4f1286481.png)\n",
    "\n",
    "Aqui começamos a ver se desenhar as características de algumas partes da imagem, vemos alta ativação em formas geométricas, como as linhas e círculos das texturas.\n",
    "\n",
    "![image](https://user-images.githubusercontent.com/56324869/162349351-614f03bf-f781-4d56-a657-56d31b474e25.png)\n",
    "\n",
    "Na terceira camada de convolução, já vemos definidamente características importantes das imagens, como as rodas do veículo, os rostos humanos, e mesmo letras, algo que não é passado como objetivo do aprendizado, mas perceba como o modelo julga importante conhecer as letras, pois é uma característica importante de determinadas classes no ImageNet, como sinais de transito, teclado de computador, super mercados e muitos outros. \n",
    "\n",
    "![image](https://user-images.githubusercontent.com/56324869/162349945-2134b56a-8544-4c83-82bd-3116db264c04.png)\n",
    "\n",
    "Aqui na quarta camada, já estamos proximos das camadas lineares, o modelo já começa a dar ênfase nas patas dos pássaros, rostos de animais, letras, roupas.\n",
    "\n",
    "\n",
    "![image](https://user-images.githubusercontent.com/56324869/162350190-8ef73dde-f9d9-4dde-98fa-798132635155.png)\n",
    "\n",
    "A última camada antes das camadas lineares, temos de maneira muita definida rostos humanos, rostos de animais, letreiros, rodas etc.\n",
    "\n",
    "\n",
    "-------\n",
    "\n",
    "Está é a abordagem moderna de como o computador compreende imagens, através do aprendizado de características, e representações da mesma procurando por suas caracterísiticas. Utilizando estas representações, alimentamos o classificador que vimos no softmax classifier e two layer neural net, definidos por camadas lineares que irão mapear estas caracterísitcas até a probabilidade associada da classe. Desta maneira, o pipeline que temos pode ser pensado como:\n",
    "\n",
    "![image](https://user-images.githubusercontent.com/56324869/162350685-617e1e9b-e4d1-4a7b-9eb7-217d228c8420.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# IMPORTANTE\n",
    "Essas são apenas algumas visualizações, diferentes modelos tendem a aprender diferentes formas de abstração. Veja como diferentes modelos olham para diferentes regiões e características de uma mesma imagem:\n",
    "\n",
    "![image](https://user-images.githubusercontent.com/56324869/162352648-8f347b07-1ea0-42b7-81bb-c157080e83c3.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Não acabamos por aqui!\n",
    "\n",
    "Re-inventar a roda não é recomendado, e devemos saber quando \"Re-inventar\" vale a pena a nivel de aprendizado. \n",
    "\n",
    "Para os próximos notebooks, utilizaremos o framework PyTorch, e verá que este, é modular, e extremamente similar a como fizemos aqui, porém com uma quantidade gigante de blocos e funcionalidades!;\n",
    "\n",
    "Veremos as ResNets e construiremos modelos robustos e atuais para classificação!"
   ]
  }
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